搜索资源列表
EM_TuXiangfenge
- 期望最大EM算法及其在混合高斯模型中的应用.caj
fc5j_EM_matlab
- em算法求解混合高斯模型，适合图像处理中，对象分割-em algorithm Gaussian mixture model suitable for image processing, object segmentation
EMGMM
- 混合高斯模型和EM算法结合，当中用到了自己写的Kmeans聚类，附带测试样例、训练样例和main函数。
MixtGaussian
- 这是基于em算法混合高斯模型参数的估计方法，用matlab开发的，看一遍程序就会明白了。-mixture Gauss estimation
EMCluster
- EM聚类算法，是学习混合高斯模型的好帮手-EM algorithm, Gaussian mixture model to learn a good helper
SSMS
- 基于高斯混合模型，经由一种高效率的EM-MAP算法，解决图像超分辨率重建问题-A general fr a mework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient M
mixturecode2
- 自适应的选择高斯混合模型个数，并用EM算法估计参数-Adaptive selection of the number of Gaussian mixture model and estimated parameters using EM algorithm
EM_Gaussian
- 基于EM算法的高斯混合模型参数估计Matlab算法，-EM algorithm-based parameter estimation of Gaussian mixture model
emgm
- EM算法用于高斯混合模型，实现数据的精确分类-The EM algorithm for Gaussian mixture model, the exact classification of the data
EMCluster
- EM高斯混合多变量模型聚类算法，直接可以运行-EM Gaussian mixture model clustering algorithm, and can be run directly
EM-suanfa-hunhegaosi
- em算法计算混合高斯模型的参数估计，极大似然，EM算法用于K均值问题的参数估计。MATLAB实现有代码-em algorithm Gaussian mixture model parameter estimation, maximum likelihood parameter estimation for K-means problem EM algorithm. MATLAB implementation code
GMM_EM
- 2类分类高斯混合模型 使用k-means的方法来初始化GMM， 基于EM算法计算出GMM模型参量。 测试GMM模型分别有2个，4个，8个混合成分-2-class classifier with Gaussian Mixture Models. Use the k-means method to initialize the GMM’s Then improve the GMM models iteratively b
GM_EM
- 经典的em算法即期望最大化算法，可用于高斯混合GMM模型和聚类算法，-Classic em algorithm that expectation maximization algorithm can be used for Gaussian mixture models and GMM clustering algorithm,
EM_GMM
- 基于EM算法实现的高斯混合模型数据分类，可以很优秀的对各种数据进行聚类分析，R语言实现-EM algorithm based on Gaussian mixture model data classification, can be very good for a variety of data clustering analysis, R language
gmeem
- 程序基于EM算法实现多维高斯混合模型的参数估计。-Parameter estimation of multi dimensional Gauss mixture model based on EM algorithm.
EmGMM
- 高斯混合模型的最大期望迭代求解算法,可用于图像区域灰度分布估计-Expectation maximazation(EM) for Gaussian mixture model(GMM)
EM_CD
- 基于高斯混合模型和EM（Expectation Maximization）算法的SAR影像变化监测算法，并附带示例。总体思路是首先将两个时期的SAR影像做log和ratio运算，生成差分影像，然后通过EM算法估计高斯混合模型的参数，最后根据高斯混合模型最大概率，生成变化监测结果。-Unsupervised change detection method for SAR images using EM algorithms of Gaus
EM_GMM
- 用EM算法对混合高斯模型中的参数进行估计 一种改进的EM算法即Monte Carlo EM算法（MCEM）的一个简单例子(The parameters in the mixed Gaussian model are estimated by EM algorithm An improved EM algorithm is a simple example of the Monte Carlo EM algorithm (MCEM))
GMM
- 高斯混合模型，通过EM算法迭代得出，可用于语音识别，图像识别等各种领域(Gauss mixture model is iteratively obtained by EM algorithm, and can be used in various fields such as speech recognition and image recognition)
GMM_EM
- GMM算法是混合高斯模型，其求解过程需要不断迭代，本程序利用EM算法进行了仿真实现，可以加深对GMM的理解。(GMM algorithm is a hybrid Gauss model, and its solution process needs iteration. This program uses EM algorithm for simulation, which can deepen the understanding of